Stiffness identification of four-point-elastic-support rigid plate

As the stiffness of the elastic support varies with the physical-chemical erosion and mechanical friction, model catastrophe of a single degree-of-freedom(DOF) isolation system may occur. A 3-DOF four-point-elastic-support rigid plate(FERP) structure is presented to describe the catastrophic isolation system. Based on the newly-established structure, theoretical derivation for stiffness matrix calculation by free response(SMCby FR) and the method of stiffness identification by stiffness matrix disassembly(SIby SMD)are proposed. By integrating the SMCby FR and the SIby SMD and defining the stiffness assurance criterion(SAC), the procedures for stiffness identification of a FERP structure(SIFERP) are summarized. Then, a numerical example is adopted for the SIFERP validation, in which the simulated tested free response data are generated by the numerical methods, and operation for filtering noise is conducted to imitate the practical application. Results in the numerical example demonstrate the feasibility and accuracy of the developed SIFERP for stiffness identification.

Friction characteristics of mechanically exfoliated and CVD-grown single-layer MoS_2

In this work, the friction characteristics of single-layer MoS2 prepared with chemical vapor deposition （CVD） at three different temperatures were quantitatively investigated and compared to those of single-layer MoS2 prepared using mechanical exfoliation. The surface and crystalline qualities of the MoS2 specimens were characterized using an optical microscope, atomic force microscope （AFM）, and Raman spectroscopy. The surfaces of the MoS2 specimens were generally flat and smooth. However, the Raman data showed that the crystalline qualities of CVD-grown single-layer MoS2 at 800 ℃ and 850 ℃ were relatively similar to those of mechanically exfoliated MoS2 whereas the crystalline quality of the CVD-grown single-layer MoS2 at 900 ℃ was lower. The CVD-grown single-layer MoS2 exhibited higher friction than mechanically exfoliated single-layer MoS2, which might be related to the crystalline imperfections in the CVD-grown MoS2. In addition, the friction of CVD-grown single-layer MoS2 increased as the CVD growth temperature increased. In terms of tribological properties, 800 ℃ was the optimal temperature for the CVD process used in this work. Furthermore, it was observed that the friction at the grain boundary was significantly larger than that at the grain, potentially due to defects at the grain boundary. This result indicates that the temperature used during CVD should be optimized considering the grain size to achieve low friction characteristics. The outcomes of this work will be useful for understanding the intrinsic friction characteristics of single-layer MoS2 and elucidating the feasibility of single-layer MoS2 as protective or lubricant layers for micro- and nano-devices.

Tribological performance of TiAlSiN and TiAlCrN/TiAlN coating blades

A new laboratory evaluation method for the coating blade was developed, and the tribological properties of coating blades with different Ti-based coatings were studied by UMT-2 tribometer. Comparison between the areas of scratches before and after the cutting experiment was used to evaluate the cutting performance of the blades. Results showed that friction coefficient of TiA1CrN/TiA1N coating was significantly lower than that of TiA1SiN coating. Analysis of the worn surface revealed that the TiA1SiN and TiAICrN/TiA1N coatings in the dry turning process exhibited sign of inhornogeneous adhesive wear and abrasive wear. TiA1CrN/TiA1N coating has a longer working life and better anti-wear property because of its duplex coating.

One neural network approach for the surrogate turbulence model in transonic flows

With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective.